This dashboard shows a handful of data visualization use cases for the Unstructured Data Analytics initiative of the Allegheny County Department of Public Health. This was developed by staff at Chapin Hall and its partners at Argonne National Laboratory.
---
title: "ACDHS Dashboard Demo"
author: "Chapin Hall"
date: "August 6, 2018"
output:
flexdashboard::flex_dashboard:
storyboard: true
social: menu
source: embed
logo: img/AGC-triangle-small.png
css: flexdash.css
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warnings = FALSE, error = FALSE)
### Load and--if necessary--install packages ----------------------------------#
package.list <- c("flexdashboard", "data.table", "dplyr", "stringr", "rgdal",
"MASS", "plotly", "leaflet")
for (p in package.list){
if (!p %in% installed.packages()[, "Package"]) install.packages(p)
library(p, character.only = TRUE)
}
### Load custom functions -----------------------------------------------------#
grepv <- function(p, x, ...) grep(p, x, value = TRUE, ...)
cn <- function(x) colnames(x)
```
### Dashboard Demonstration
This dashboard shows a handful of data visualization use cases for the Unstructured Data Analytics initiative of the Allegheny County Department of Public Health. This was developed by staff at Chapin Hall and its partners at Argonne National Laboratory.
- This was made with the free and open-source [R programming language](https://www.r-project.org/about.html)
- Code-driven dashboards like this can be automatically generated to be custom for any given user, and updated with new inputs, analysis, and visualization as part of the full analysis pipeline
- The code and data used to create this example can be found—and reused and contributed to—at [this open-source GitHub repository](https://github.com/chapinhall/acdhs-demo)
- **Note** that all data here is fake, intended only for the purpose of illustration
### Case-Level Visualization
***
- This figure combines
- text-mined sentiment score
- text-mined key topics of relevance
- indication of institutional contacts, including DHS investigation and intervention
- Low sentiment scores could be used to flag potential consultation opportunities with a nurse or substance abuse counselor, when combined with key topic combinations
- Topic and sentiment scores can help supervisors consider check-in priority and frequency, and quickly review case trajectories
- Persistence of key topics can raise flags to QA teams to complete additional modules, e.g. related to housing concerns
### System-Level Trajectory Visualization
***
* Trajectory visualizations can show pathways of cases through institutional contacts
* This combined plot shows how clustering methods were used to classify clients into distinct groupings based on the start, duration, intermittence, and types of involvements
### System-Level Topic Map
```{r display zipcode map with topic layers}
# Get zipcode boundary data
zips <- readOGR(dsn = "data", layer = "Allegheny_County_Zip_Code_Boundaries", verbose = FALSE)
# Sample topics:
topics <- c("Pregnant", "Domestic Violence", "Opiates", "Suicide", "Low Income")
sigma <- matrix(c(1.0, 0.2, 0.1, 0.2, 0.4,
0.2, 1.0, 0.3, 0.4, 0.5,
0.1, 0.3, 1.0, 0.4, 0.6,
0.2, 0.4, 0.4, 1.0, 0.4,
0.4, 0.5, 0.6, 0.4, 2.0), nrow = 5)
mu <- c(0.8, 1.5, 0.0, -1.0, 1.0)
# Draw correlated values
set.seed(8062018)
draws <- mvrnorm(n = nrow(zips@data), mu = mu, Sigma = sigma)
# Put this on a 0-100 scale, label, and arrange in order (to give a pattern to matching with zips)
draws <- round((draws - min(draws))/(max(draws) - min(draws))*100, 1)
colnames(draws) <- topics
draws <- arrange(as.data.frame(draws), -`Low Income`)
# Attach values to zipcodes
zips@data <-
cbind(zips@data, draws) %>%
within(topicPop <- paste0("Topic Scores for Zip ", ZIP, ":
",
"Pregnant: ", sprintf("%1.1f", Pregnant), "
",
"Domestic Violence: ", sprintf("%1.1f", `Domestic Violence`), "
",
"Opiates: ", sprintf("%1.1f", Opiates), "
",
"Suicide: ", sprintf("%1.1f", Suicide), "
",
"Low Income: ", sprintf("%1.1f", `Low Income`)))
palBin_blue <- colorBin(palette = "Blues", domain = c(0, 100), bins = 5)
myOpacity <- 0.7
leaflet(data = zips) %>% # width = "100%"
#addTiles() %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(group = "Pregnant", fillColor = ~palBin_blue(Pregnant),
popup = ~topicPop, weight = 2.0, fillOpacity = myOpacity) %>%
addPolygons(group = "Domestic Violence", fillColor = ~palBin_blue(`Domestic Violence`),
popup = ~topicPop, weight = 2.0, fillOpacity = myOpacity) %>%
addPolygons(group = "Opiates", fillColor = ~palBin_blue(Opiates),
popup = ~topicPop, weight = 2.0, fillOpacity = myOpacity) %>%
addPolygons(group = "Suicide", fillColor = ~palBin_blue(Suicide),
popup = ~topicPop, weight = 2.0, fillOpacity = myOpacity) %>%
addPolygons(group = "Low Income", fillColor = ~palBin_blue(`Low Income`),
popup = ~topicPop, weight = 2.0, fillOpacity = myOpacity) %>%
addLayersControl(baseGroups = c("Pregnant", "Domestic Violence", "Opiates", "Suicide", "Low Income"),
options = layersControlOptions(collapsed = FALSE)) %>%
addLegend(position = "bottomleft", colors = unique(palBin_blue(0:100)),
title = "Topic Scores",
labels = c("0-20", "20-40", "40-60", "60-80", "80-100"))
```
***
* Dynamic maps offer panning, zooming, and "popup" annotations that allow exploration of geographic trends in various case topics and combinations thereof, such as "opiod" and "overdose"
* This map shows an example of multiple topic layers for Alleghency county zip codes, with more information available through clicking